一个基于改进的CEEMDAN的碳排放多因素组合预测模型
Guohui Li1, Hao Wu2, Hong Yang2
1School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China. lghcd@163.com.
Environmental science and pollution research international
|February 21, 2024
概括
准确的碳排放预测对于气候目标至关重要. 一个新的ICEEMDAN-LOBiLSTM-LOLSSVM模型通过结合分解,优化深度学习和支持矢量机来提高准确性来改善预测.
科学领域:
- 环境科学 环境科学
- 气候变化建模模型
- 数据科学数据科学数据科学
背景情况:
- 全球温室气体排放量不断上升,需要对气候政策进行准确的预测.
- 实现碳中和和峰值排放需要可靠的预测模型.
研究的目的:
- 开发和验证一种新的混合模型,用于精确预测碳排放.
- 通过整合先进的信号处理和机器学习技术来提高预测准确度.
主要方法:
- 特征选择的斯皮尔曼相关系数.
- 改进了完整的集体实证模式分解与自适应噪声 (ICEEMDAN) 进行信号分解.
- 莱默斯优化器 (LO) 优化了双向长短期内存 (BiLSTM) 和最小平方支持矢量机 (LSSVM) 进行预测.
- 用于间隔预测的内核密度估计.
主要成果:
- 拟议的ICEEMDAN-LOBiLSTM-LOLSSVM模型在其他九种模型中表现出优越的性能.
- 在中国取得了极好的准确度指标 (RMSE:0.4468,MAE:0.3612,MAPE:0.0120,R2:0.9839).
- 模型验证证实了中国和德国碳排放数据的有效性.
结论:
- 混合ICEEMDAN-LOBiLSTM-LOLSSVM模型为碳排放预测提供了强大的和准确的方法.
- 这种方法为有效的气候变化减缓战略和政策制定提供了坚实的基础.
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